Seasonal active landsliding and hillslope activity in the southern Central Andes of NW Argentina

2020 ◽  
Author(s):  
Mohammad M.Aref ◽  
Bodo Bookhagen ◽  
Taylor T. Smith ◽  
Manfred R. Strecker
Keyword(s):  
Time Series ◽  
Driving Forces ◽  
Central Andes ◽  
Temporal Variations ◽  
High Elevation ◽  
Optical Data ◽  
Tropospheric Delay ◽  
Active Layer Thickness ◽  
Weather Model ◽  
3D Deformation

<p>The eastern Central Andes of northwestern Argentina is characterized by a steep topographic gradient with elevations ranging from 1000m in the foreland to more than 6000m in the eastern Andean Cordillera. This setting furthermore shows high topographic relief with deeply incised river valleys that are frequently impacted by strong rainfall events driven by the South American monsoon. Additionally, a strong vegetation cover contrast from dense coverage in the low elevation foreland to sparse coverage at high elevation defines the environmental gradient in this area. This area is impacted by several types of hillslope instabilities and landsliding: at some high elevations above 5000m hillslope instability are related to solifluction processes, whereas shallow and deep seated landsliding affect geologically preconditioned areas.</p><p>Here we use a combination of different radar sensors and wavelengths to describe the 3D deformation signal of instable hillslopes: TerraSAR-X, Sentinel-1, and ALOS2. To mitigate the tropospheric delay from InSAR measurements, phase-based and weather model approaches are applied to improve the spatial and temporal variations of displacement signals.  We use persistent and small baseline subsets (SBAS) category of distributed scatterer approaches to derive deformation fields and we invert for 3D deformation fields using several look angles in combination with GNSS data under different assumptions including that the horizontal component has a motion parallel to the downhill slope. We analyze Line-of-sight (LOS) time series and combine deformation fields with temperature and rainfall measurements to better understand driving forces of high-elevation hillslope instabilities We describe two deep-seated landslides with downslope velocities exceeding 5-10 cm/yr and we exploit image-cross correlation techniques of optical data to monitor seasonal and inter-annual changes. The periodic changes of InSAR deformation and temperature time series show freeze-thaw processes of the active layer thickness of the permafrost areas at elevations exceeding 5000m. We document that deep-seated, fast moving landslides are related to geologic preconditioning. The combination of SAR and optical approaches helps to describe hillslope regimes in steep and difficult to access terrain.</p>

Towards a new generation of Generic Atmospheric Correction Online Service for InSAR (GACOS 2.0)

2020 ◽  
Author(s):  
Chen Yu ◽  
Zhenhong Li
Keyword(s):  
Time Series ◽  
Atmospheric Correction ◽  
Tropospheric Delay ◽  
Online Service ◽  
Atmospheric Effect ◽  
Automatic Data ◽  
Gps Network ◽  
Weather Model ◽  
New Generation ◽  
Globally Distributed

<div>The tremendous development of InSAR missions (e.g., Sentinel-1A/1B, ALOS-2, TerraSAR-X/TanDEM-X, COSMO-SkyMED, RADARSAT-2, and Gaofen-3) in recent years facilitates the study of smaller amplitude ground deformation using longer time series and over greater spatial scales. This poses new challenges for correcting interferograms for atmospheric (tropospheric) effects especially the dominant long wavelength effect and the spatial-temporal correlated topographic related effect, resulting the atmospheric effect being distance-dependent with larger interferograms experiencing greater contamination and preventing deformation mapping of large scales deformation phenomena such as inter-seismic tectonic strain accumulation, post-seismic relaxation of fault systems and Glacial Isostatic Adjustment (GIA). </div><div> </div><div>To overcome this, we have released the Generic Atmospheric Correction Online Service (GACOS) whose notable features comprise: (i) global coverage, (ii) all-weather, all-time usability, (iii) correction maps available in near real-time, and (iv) indicators to assess the correction performance and feasibility. The model applies operational high resolution ECMWF data (0.125-degree grid, 137 vertical levels, 6-hour interval) using an iterative tropospheric decomposition model and its performance for InSAR atmospheric correction was tested using globally-distributed interferograms, encompassing both flat and mountainous topographies, mid-latitude and near-polar regions, monsoon and oceanic climate systems, achieving a phase precision and displacement accuracy of approximately 1 cm for the corrected interferograms. Indicators describing the model’s performance including (i) ECMWF cross-RMS, (ii) phase-delay correlations, (iii) ECMWF time differences, and (iv) topography variations, were developed to provide quality control for subsequent automatic processing and provide insights of the confidence level with which the generated atmospheric correction maps may be applied. </div><div> </div><div>To further improve the performance of GACOS to better serve the InSAR community, a new generation (GACOS 2.0) is being developed by: (i) improving the temporal resolution by integrating the newly published 1-hour ERA-5 weather model and the 5-minute GPS tropospheric delay estimates; (ii) developing an API system to facilitate automatic data processing; and (iii) enhancing GACOS based on regional/local datasets (such as national weather model and regional GPS network). The ERA-5 product and global GPS tropospheric delay estimates are carefully validated in order to achieve a robust integration. Based on the globally distributed GPS network and the MODIS PWV product, the performance of GACOS 2.0 in different regions of the world is evaluated with its elevation and latitude dependency being concluded which could be served as another performance indicator. All these features will contribute to a simplified time series analysis method (i.e. relying less on spatial-temporal filters) to reduce the computational burden, provided that the majority of the atmospheric error has been mitigated by GACOS 2.0. </div><div> </div>

scholarly journals Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

2021 ◽  
Vol 13 (3) ◽  
pp. 409
Author(s):  
Howard Zebker
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Artifact Reduction ◽  
Interferometric Synthetic Aperture Radar ◽  
Spatial Properties ◽  
Model Free ◽  
Weather Model ◽  
Phase Variations ◽  
Source Of Error ◽  
Signal Correlation

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.

scholarly journals Three-dimensional deformation time series of glacier motion from multiple-aperture DInSAR observation

2019 ◽  
Vol 93 (12) ◽  
pp. 2651-2660 ◽  
Author(s):  
Sergey Samsonov
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Ground Deformation ◽  
Three Dimensional ◽  
Data Sets ◽  
Ice Flow ◽  
The North ◽  
Glacier Ice ◽  
Deformation Component ◽  
3D Deformation

AbstractThe previously presented Multidimensional Small Baseline Subset (MSBAS-2D) technique computes two-dimensional (2D), east and vertical, ground deformation time series from two or more ascending and descending Differential Interferometric Synthetic Aperture Radar (DInSAR) data sets by assuming that the contribution of the north deformation component is negligible. DInSAR data sets can be acquired with different temporal and spatial resolutions, viewing geometries and wavelengths. The MSBAS-2D technique has previously been used for mapping deformation due to mining, urban development, carbon sequestration, permafrost aggradation and pingo growth, and volcanic activities. In the case of glacier ice flow, the north deformation component is often too large to be negligible. Historically, the surface-parallel flow (SPF) constraint was used to compute the static three-dimensional (3D) velocity field at various glaciers. A novel MSBAS-3D technique has been developed for computing 3D deformation time series where the SPF constraint is utilized. This technique is used for mapping 3D deformation at the Barnes Ice Cap, Baffin Island, Nunavut, Canada, during January–March 2015, and the MSBAS-2D and MSBAS-3D solutions are compared. The MSBAS-3D technique can be used for studying glacier ice flow at other glaciers and other surface deformation processes with large north deformation component, such as landslides. The software implementation of MSBAS-3D technique can be downloaded from http://insar.ca/.

scholarly journals Nonstationary time series prediction combined with slow feature analysis

2015 ◽  
Vol 22 (4) ◽  
pp. 377-382 ◽  
Author(s):  
G. Wang ◽  
X. Chen
Keyword(s):  
Time Series ◽  
Predictive Model ◽  
Driving Forces ◽  
Time Series Prediction ◽  
Nonstationary Time Series ◽  
Feature Analysis ◽  
State Variables ◽  
Slow Feature Analysis ◽  
Almost All ◽  
External Driving

Abstract. Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

scholarly journals Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China

2017 ◽  
Vol 21 (11) ◽  
pp. 5805-5821 ◽  
Author(s):  
Fan Yang ◽  
Hui Lu ◽  
Kun Yang ◽  
Jie He ◽  
Wei Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Variations ◽  
Spatiotemporal Variability ◽  
Shortwave Radiation ◽  
Data Sets ◽  
Precipitation Data ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Precipitation and shortwave radiation play important roles in climatic, hydrological and biogeochemical cycles. Several global and regional forcing data sets currently provide historical estimates of these two variables over China, including the Global Land Data Assimilation System (GLDAS), the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the China Meteorological Forcing Dataset (CMFD). The CN05.1 precipitation data set, a gridded analysis based on CMA gauge observations, also provides high-resolution historical precipitation data for China. In this study, we present an intercomparison of precipitation and shortwave radiation data from CN05.1, CMFD, CLDAS and GLDAS during 2008–2014. We also validate all four data sets against independent ground station observations. All four forcing data sets capture the spatial distribution of precipitation over major land areas of China, although CLDAS indicates smaller annual-mean precipitation amounts than CN05.1, CMFD or GLDAS. Time series of precipitation anomalies are largely consistent among the data sets, except for a sudden decrease in CMFD after August 2014. All forcing data indicate greater temporal variations relative to the mean in dry regions than in wet regions. Validation against independent precipitation observations provided by the Ministry of Water Resources (MWR) in the middle and lower reaches of the Yangtze River indicates that CLDAS provides the most realistic estimates of spatiotemporal variability in precipitation in this region. CMFD also performs well with respect to annual mean precipitation, while GLDAS fails to accurately capture much of the spatiotemporal variability and CN05.1 contains significant high biases relative to the MWR observations. Estimates of shortwave radiation from CMFD are largely consistent with station observations, while CLDAS and GLDAS greatly overestimate shortwave radiation. All three forcing data sets capture the key features of the spatial distribution, but estimates from CLDAS and GLDAS are systematically higher than those from CMFD over most of mainland China. Based on our evaluation metrics, CLDAS slightly outperforms GLDAS. CLDAS is also closer than GLDAS to CMFD with respect to temporal variations in shortwave radiation anomalies, with substantial differences among the time series. Differences in temporal variations are especially pronounced south of 34° N. Our findings provide valuable guidance for a variety of stakeholders, including land-surface modelers and data providers.

Forest disturbances detection in Vietnam, Cambodia and Laos using Sentinel-1 data

2021 ◽  
Author(s):  
Stéphane Mermoz ◽  
Alexandre Bouvet ◽  
Marie Ballère ◽  
Thierry Koleck ◽  
Thuy Le Toan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
French Guiana ◽  
The United States ◽  
Habitat Destruction ◽  
Detection Methods ◽  
Optical Data ◽  
Forest Loss ◽  
Forest Disturbances ◽  
Synthetic Aperture Radar Data

<p>Over the last 25 years, the world’s forests have undergone substantial changes. Deforestation and forest degradation in particular contribute greatly to biodiversity loss through habitat destruction, soil erosion, terrestrial water cycle disturbances and anthropogenic CO2 emissions. In certain regions and countries, the changes have been more rapid, which is the case in the Greater Mekong sub-region recognized as deforestation hotspot (FAO, 2020). In this region, illegal and unsustainable logging and conversion of forests for agriculture, construction of dams and infrastructure are the direct causes of deforestation. Effective tools are therefore urgently needed to survey illegal logging operations which cause widespread concern in the region.</p><p>Monitoring systems based on optical data, such as the UMD/GLAD Deforestation alerts implemented on the Global Forest Watch platform, are limited by the important cloud cover which causes delays in the detections. However, it has been demonstrated in the last few years that forest losses can be timely monitored using dense time series of (synthetic aperture) radar data acquired by Sentinel-1 satellites, developed in the frame of the European Union’s Earth observation Copernicus programme. Ballère et al. (2021) showed for example that 80% of the forest losses due to gold mining in French Guiana are detected first by Sentinel-1-based forest loss detection methods compared with optical-based methods, sometimes by several months. Methods based on Sentinel-1 have been successfully applied at the local scale (Bouvet et al., 2018, Reiche et al., 2018) and can be adapted and tested at the national scale (Ballère et al., 2020).</p><p>We show here the main results of the SOFT project funded by ESA in the frame of the EO Science for Society open calls. The overall SOFT project goal is to provide validated forest loss maps every month over Vietnam, Cambodia and Laos with a minimum mapping unit of 0.04 ha, using Sentinel-1 data. The results confirm the analysis of the deforestation fronts published recently by the WWF (Pacheco et al., 2021), showing that Eastern Cambodia, and Southern and Northern Laos are currently forest disturbances hotspots.</p><p> </p><p>References:</p><p>Ballère et al., (2021). SAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery. <em>Remote Sensing of Environment</em>, <em>252</em>, 112159.</p><p>Bouvet et al., (2018). Use of the SAR shadowing effect for deforestation detection with Sentinel-1 time series. <em>Remote Sensing</em>, <em>10</em>(8), 1250.</p><p>FAO. Global Forest Resources Assessment; Technical Report; Food and Agriculture Association of the United-States: Rome, Italy, 2020.</p><p>Pacheco et al., 2021. Deforestation fronts: Drivers and responses in a changing world. WWF, Gland, Switzerland</p><div>Reiche et al., (2018). Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. <em>Remote Sensing of Environment</em>, <em>204</em>, 147-161.</div>

Evolution of Large Bedrock Landslides in the South-Central Andes of NW Argentina 

2021 ◽  
Author(s):  
Mohammad M.Aref ◽  
Bodo Bookhagen ◽  
Manfred R. Strecker
Keyword(s):  
Driving Forces ◽  
Central Andes ◽  
Anthropogenic Sources ◽  
High Temporal Resolution ◽  
Electron Content ◽  
Infrastructure Development ◽  
The South ◽  
Total Electron ◽  
Groundwater Levels ◽  
South Central

<p>Deep-seated, slow moving bedrock landslides are significant natural disasters with severe socio-economic repercussions. During the past decades, an acceleration of these hazards has been reported globally due to changes in seasonal freeze-thaw cycles, permafrost thawing, infrastructure development and other anthropogenic sources, changes of precipitation and groundwater levels, and variation in seismic activity. Interferometric Synthetic Aperture Radar(InSAR) is a powerful tool to map landslides movement from space and the Sentinel 1 C-band radar mission provides a high temporal resolution data source to investigate seasonal and intra-annual changes of landslide behaviour.</p><p>To construct a 2D/3D displacement field, we decompose a combination of different look angles and InSAR ascending and descending tracks of different sensors including Sentinel and ALOS 1 PALSAR data. The ionospheric delay for InSAR observations is estimated with a split range-spectrum technique because significant ionospheric total electron content variation is common in our study area in the Central Andes. Both statistical phase-based and weather model estimation approaches are implemented to minimize the effect of tropospheric signal on InSAR observations.</p><p>Our observations identify several areas with rapid translational slide movements exceeding 5-10 cm/y. Multi-annual and inter-annual behaviour of deformation is extracted through time series analysis and a hierarchical clustering approach is used to identify geographic areas with similar characteristics and rates. We show the wide-spread spatial distribution of unstable hill slopes in the Eastern Cordillera of the south-central Andes, especially at high elevations where field observations are difficult. We identify driving forces to be a combination of pre-existing geologic structures and climatic parameters.</p>

Comparison of Tropospheric Zenith Wet Delay from VLBI and GNSS Estimations

2021 ◽  
Author(s):  
Vicky Jia Liu ◽  
Maaria Nordman ◽  
Nataliya Zubko
Keyword(s):  
Time Series ◽  
Seasonal Variations ◽  
Elevation Angle ◽  
Correlation Coefficients ◽  
Satellite System ◽  
Tropospheric Delay ◽  
Polar Regions ◽  
Long Baseline ◽  
Zenith Wet Delay ◽  
Space Geodetic Techniques

<p>Tropospheric delay is one of the major error sources for space geodetic techniques such as Very Long Baseline Interferometry (VLBI) and Global Navigation Satellite System (GNSS). In this study, we compared the agreement of tropospheric zenith wet delay (ZWD) seasonal variations derived from VLBI and GNSS observations at 8 stations that are located at all around the globe. We have analysed time series of 8 years, starting in 2012 until end of 2019. Results show that VLBI_ZWD present clear seasonal variations which depend on the location of each station, in the tropics the variability is more pronounced than in mid-latitudes or polar regions. Furthermore, the VLBI_ZWD also shows a reasonably good agreement with seasonal fit model. When comparing zenith wet delays derived from co-located GNSS and VLBI stations at  cut-off elevation angle, they agree quite well, which is proved by the high correlation coefficients, varying from 0.6 up to 0.95. The biases between the techniques are in mm level and standard errors of the whole time series are in few centimetres.</p>

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